@inproceedings{chan-etal-2019-modeling,
title = "Modeling Personalization in Continuous Space for Response Generation via Augmented {W}asserstein Autoencoders",
author = "Chan, Zhangming and
Li, Juntao and
Yang, Xiaopeng and
Chen, Xiuying and
Hu, Wenpeng and
Zhao, Dongyan and
Yan, Rui",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1201",
doi = "10.18653/v1/D19-1201",
pages = "1931--1940",
abstract = "Variational autoencoders (VAEs) and Wasserstein autoencoders (WAEs) have achieved noticeable progress in open-domain response generation. Through introducing latent variables in continuous space, these models are capable of capturing utterance-level semantics, e.g., topic, syntactic properties, and thus can generate informative and diversified responses. In this work, we improve the WAE for response generation. In addition to the utterance-level information, we also model user-level information in latent continue space. Specifically, we embed user-level and utterance-level information into two multimodal distributions, and combine these two multimodal distributions into a mixed distribution. This mixed distribution will be used as the prior distribution of WAE in our proposed model, named as PersonaWAE. Experimental results on a large-scale real-world dataset confirm the superiority of our model for generating informative and personalized responses, where both automatic and human evaluations outperform state-of-the-art models.",
}
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%0 Conference Proceedings
%T Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders
%A Chan, Zhangming
%A Li, Juntao
%A Yang, Xiaopeng
%A Chen, Xiuying
%A Hu, Wenpeng
%A Zhao, Dongyan
%A Yan, Rui
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chan-etal-2019-modeling
%X Variational autoencoders (VAEs) and Wasserstein autoencoders (WAEs) have achieved noticeable progress in open-domain response generation. Through introducing latent variables in continuous space, these models are capable of capturing utterance-level semantics, e.g., topic, syntactic properties, and thus can generate informative and diversified responses. In this work, we improve the WAE for response generation. In addition to the utterance-level information, we also model user-level information in latent continue space. Specifically, we embed user-level and utterance-level information into two multimodal distributions, and combine these two multimodal distributions into a mixed distribution. This mixed distribution will be used as the prior distribution of WAE in our proposed model, named as PersonaWAE. Experimental results on a large-scale real-world dataset confirm the superiority of our model for generating informative and personalized responses, where both automatic and human evaluations outperform state-of-the-art models.
%R 10.18653/v1/D19-1201
%U https://aclanthology.org/D19-1201
%U https://doi.org/10.18653/v1/D19-1201
%P 1931-1940
Markdown (Informal)
[Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders](https://aclanthology.org/D19-1201) (Chan et al., EMNLP-IJCNLP 2019)
ACL